Telegram-Image-classifier

This project implements a Convolutional Neural Network (CNN) for image classification using TensorFlow. It integrates with Telegram to classify images sent by users into predefined categories using the CIFAR-10 dataset. The CNN model is trained on the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes. Creating a detailed README.md file is crucial for your project on GitHub. It helps users understand what your project does, how to set it up, and how to use it effectively. Below is a step-by-step procedure for writing a comprehensive README.md for your image classification project using CNN with TensorFlow and Telegram integration.

Project Name: telegram-image-classifier

Project Overview

This project implements a Convolutional Neural Network (CNN) for image classification using TensorFlow. It integrates with Telegram to classify images sent by users into predefined categories using the CIFAR-10 dataset. The CNN model is trained on the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes.

Table of Contents

1. Installation
2. Usage
3. Commands
4. Contributing
5. License

Installation

Follow these steps to set up the project on your local machine:

Your System must Run the python version -- 3.9.6

1. Clone the Repository:

   git clone https://github.com/VishnuPrasanth-A/Telegram-Image-classifier.git
   cd telegram-image-classifier

2. Install Dependencies:

   pip install -r requirements.txt

3. Set Up Telegram Bot Token:

   • Create a token.txt file in the project directory.
   • Paste your Telegram bot token obtained from the BotFather into token.txt and save it.

Usage

To run the Telegram image classifier bot, follow these steps:

1. Start the Application:

   python main.py

2. Interact with the Telegram Bot:

   • Open Telegram and search for your bot using the bot's username.
   • Use the following commands to interact with the bot:
     • /start: Starts the conversation with the bot.
     • /help: Displays the available commands and their descriptions.
     • /train: Trains the neural network using the CIFAR-10 dataset.

3. Classify Images:

   • Send an image file (photo) to the bot.
   • The bot will process the image and reply with the predicted class of the image based on the trained CNN model.

Commands

• /start: Initiates the conversation with the bot.
• /help: Provides information about available commands.
• /train: Trains the neural network model using the CIFAR-10 dataset.

Contributing

Contributions are welcome! Here's how you can contribute to this project:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/improvement).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature/improvement).
6. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Feel free to customize the README.md further based on additional features, setup instructions, or specific details of your project. Providing clear instructions and examples will help users understand and use your project effectively.